{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74a58616",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" \n",
    "\n",
    "# 解决坐标轴刻度负号乱码\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ac9d28d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n",
       "0          2       3  12669   9656     7561     214              2674   \n",
       "1          2       3   7057   9810     9568    1762              3293   \n",
       "2          2       3   6353   8808     7684    2405              3516   \n",
       "3          1       3  13265   1196     4221    6404               507   \n",
       "4          2       3  22615   5410     7198    3915              1777   \n",
       "..       ...     ...    ...    ...      ...     ...               ...   \n",
       "435        1       3  29703  12051    16027   13135               182   \n",
       "436        1       3  39228   1431      764    4510                93   \n",
       "437        2       3  14531  15488    30243     437             14841   \n",
       "438        1       3  10290   1981     2232    1038               168   \n",
       "439        1       3   2787   1698     2510      65               477   \n",
       "\n",
       "     Delicassen  \n",
       "0          1338  \n",
       "1          1776  \n",
       "2          7844  \n",
       "3          1788  \n",
       "4          5185  \n",
       "..          ...  \n",
       "435        2204  \n",
       "436        2346  \n",
       "437        1867  \n",
       "438        2125  \n",
       "439          52  \n",
       "\n",
       "[440 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data = pd.read_csv('Wholesale customers data.csv')\n",
    "user_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d149904d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   Channel           440 non-null    int64\n",
      " 1   Region            440 non-null    int64\n",
      " 2   Fresh             440 non-null    int64\n",
      " 3   Milk              440 non-null    int64\n",
      " 4   Grocery           440 non-null    int64\n",
      " 5   Frozen            440 non-null    int64\n",
      " 6   Detergents_Paper  440 non-null    int64\n",
      " 7   Delicassen        440 non-null    int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 27.6 KB\n"
     ]
    }
   ],
   "source": [
    "user_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dfeb5b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.774272</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count    440.000000        440.000000    440.000000  \n",
       "mean    3071.931818       2881.493182   1524.870455  \n",
       "std     4854.673333       4767.854448   2820.105937  \n",
       "min       25.000000          3.000000      3.000000  \n",
       "25%      742.250000        256.750000    408.250000  \n",
       "50%     1526.000000        816.500000    965.500000  \n",
       "75%     3554.250000       3922.000000   1820.250000  \n",
       "max    60869.000000      40827.000000  47943.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c77d9061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.70833271, 0.53987376, 0.42274083, 0.01196489, 0.14950522,\n",
       "        0.07480852],\n",
       "       [0.44219826, 0.61470384, 0.59953989, 0.11040858, 0.20634248,\n",
       "        0.11128583],\n",
       "       [0.39655169, 0.5497918 , 0.47963217, 0.15011913, 0.2194673 ,\n",
       "        0.48961931],\n",
       "       ...,\n",
       "       [0.36446153, 0.38846468, 0.7585445 , 0.01096068, 0.37223685,\n",
       "        0.04682745],\n",
       "       [0.93773743, 0.1805304 , 0.20340427, 0.09459392, 0.01531   ,\n",
       "        0.19365326],\n",
       "       [0.67229603, 0.40960124, 0.60547651, 0.01567967, 0.11506466,\n",
       "        0.01254374]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "user_data_Norm =Normalizer().fit_transform(user_data.iloc[:,2:]) # 删除销售渠道和销售地区两列数据\n",
    "user_data_Norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cbfdfc7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2315513b988>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x23154f8ee48>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans \n",
    "from sklearn.metrics import silhouette_score #轮廓系数\n",
    "score=[]\n",
    "model=[]\n",
    "#将k值从2变化到7\n",
    "for i in range(2,7):\n",
    "    cluster= KMeans(n_clusters=i, random_state=0).fit(user_data_Norm)\n",
    "    score.append(silhouette_score(user_data_Norm,cluster.labels_))\n",
    "    model.append(cluster)\n",
    "                 \n",
    "plt.plot(range(2,7),score)\n",
    "#给k最大的位置加虚线   #idxmax()[] 取最大索引  因为这边从k=2开始 所以+2\n",
    "plt.axvline(pd.DataFrame(score).idxmax()[0]+2,ls=':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "74d9632d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.264083</td>\n",
       "      <td>0.479221</td>\n",
       "      <td>0.661907</td>\n",
       "      <td>0.134295</td>\n",
       "      <td>0.251450</td>\n",
       "      <td>0.105409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.869573</td>\n",
       "      <td>0.174954</td>\n",
       "      <td>0.226061</td>\n",
       "      <td>0.224904</td>\n",
       "      <td>0.050075</td>\n",
       "      <td>0.074002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicassen\n",
       "0  0.264083  0.479221  0.661907  0.134295          0.251450    0.105409\n",
       "1  0.869573  0.174954  0.226061  0.224904          0.050075    0.074002"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#建模\n",
    "center = pd.DataFrame(model[0].cluster_centers_,columns=['Fresh','Milk','Grocery','Frozen','Detergents_Paper','Delicassen'])\n",
    "center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "145ea16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      0\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "435    1\n",
       "436    1\n",
       "437    0\n",
       "438    1\n",
       "439    0\n",
       "Length: 440, dtype: int32"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km_labels = pd.Series(model[0].labels_)\n",
    "# sizes = km_labels.value_counts(1).values\n",
    "# labels = km_labels.value_counts(1).index\n",
    "# plt.pie(sizes,autopct='%.f%%')\n",
    "# plt.legend(labels)\n",
    "km_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c6e4ebfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2, random_state=0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1737fcb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 648x648 with 0 Axes>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x2315ad63e08>,\n",
       "  <matplotlib.patches.Wedge at 0x2315ad77088>],\n",
       " [Text(-0.23382180021085588, 1.0748615565486352, ''),\n",
       "  Text(0.23382180021085577, -1.0748615565486352, '')],\n",
       " [Text(-0.12753916375137592, 0.586288121753801, '56.82%'),\n",
       "  Text(0.12753916375137586, -0.586288121753801, '43.18%')])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2315ad091c8>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "data_series = pd.Series(model[0].labels_)\n",
    "plt.pie(data_series.value_counts(1).values,autopct='%.2f%%')\n",
    "plt.legend(data_series.value_counts(1).index,prop={'size':20})\n",
    "plt.rcParams['font.size'] = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "be6713dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23157f199c8>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, '0'), Text(1, 0, '1')])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([0. , 0.2, 0.4, 0.6, 0.8, 1. ]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "center.plot(kind='bar',figsize=(10,10))\n",
    "plt.legend(loc='upper left',prop={'size':15})\n",
    "plt.xticks(fontsize=20,rotation=0)\n",
    "plt.yticks(fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcacaa80",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da0673f1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
